Thinking Like Transformers

In this episode: • The Transformer's Black Box: Linda introduces the 'Thinking Like Transformers' paper, highlighting the challenge of understanding the computational model behind transformers, unlike RNNs and their connection to finite state machines. Professor Norris agrees, sharing a witty remark about the opacity of modern deep learning models. • Introducing RASP: A Language for Transformers: Linda explains the core concept of RASP (Restricted Access Sequence Processing Language), a programming language designed to mirror the information flow of a transformer. She details the main operations: element-wise computations, and the crucial 'select' and 'aggregate' pair that mimics attention. • From Code to Heads: RASP in Action: To make the concepts concrete, Linda walks through a simple RASP program from the paper, such as creating a histogram of tokens. They discuss the key insight that a RASP program can be 'compiled' to estimate the number of layers and attention heads a transformer would need for the task. • Implications and Insights: The hosts explore the broader implications of the RASP model, such as analyzing the expressive power of restricted-attention models and explaining empirical results like the 'Sandwich Transformer'. Professor Norris is particularly intrigued by how this formal model can explain real-world phenomena. • Thinking Like a Researcher: Professor Norris and Linda summarize the paper's contributions, agreeing that RASP provides a powerful conceptual tool for reasoning about transformer capabilities. Linda concludes by mentioning the publicly available RASP REPL for listeners who want to experiment themselves.